| 摘要: | 本文的目的在於提出一個稱為逐步分解迴歸分析法的新方法，來改善傳統的多變數迴歸分析法的在易理解性、通用性、應用性、模型彈性等方面的缺點。其原理是先假設房地產的每坪單價是供需圈內平均每坪單價與多個因子的無因次的調整係數的連乘積，因此這個方法由最重要的因子開始，逐一分解各因子的調整係數之估計值，並建立各調整係數的預測模型。這個方法包含三個步驟：(1)排序：以排序等分法估計因子的重要性。(2)分解：以逐步分解法建構各因子的調整係數與因子之間的單變數迴歸模型。(3)整合：將各因子的調整係數之迴歸模型整合為勘估標的價格預測模型。本研究的因變數為住宅房屋成交時之每坪單價。自變數有代表運輸功能的影響之距離最近捷運站的距離，代表的生活功能的影響之徒步生活圈內的超商數，代表房子室內居住品質的影響之屋齡，代表市場趨勢的影響之交屋年月，以及表示空間位置的影響的地理位置(縱座標、橫座標)。研究樣本取自台北市的二個行政區，以及新北市的二個行政區，一共四個供需圈，分成四個資料集。結論顯示，逐步分解迴歸分析法比傳統的多變數迴歸分析法有更佳的易理解性、通用性、應用性、模型彈性，並有更高的準確度。
The purpose of this paper is to propose a new method called stepwise decomposition regression analysis method to overcome the shortcomings of traditional multivariate regression analysis in easy understanding, versatility, application, model elasticity and so on. The principle is to assume that the price per unit area of real estate is the average price per unit area of the specific circle of housing supply and demand multiplied by the product of several dimensionless adjustment coefficients of factors. This method starts from the most important factor, then one by one, to decompose the estimated adjustment coefficient of each factor, and build the predictive model for each adjustment coefficient. This method consists of three steps: (1) Sorting: Employ sorting and grouping approach to estimate the importance of the factors. (2) Decomposition: Use the stepwise decomposition approach to construct the single variable regression model for each adjustment coefficients to its factor. (3) Integration: Integrate the adjustment coefficient regression models to a real estate price valuation model. The dependent variable in this study is the residential housing price per unit area. The independent variables include the distance to the nearest MRT station which represents the impact of transportation function, the number of convenience stores in the living circle on foot which represents the impact of living function in the living circle on foot, the age of house which represents the impact of living function in room, the transaction date which represents the impact of market trend, and the geographic coordinates which represent the impact of spatial location. The samples are collected from two districts in Taipei City, and two districts in New Taipei City, totally four circles of supply and demand, and are divided into four data sets. The results show that the stepwise decomposition regression analysis is better in easy understanding, versatility, application, model flexibility, and reach a higher degree of accuracy than conventional multivariate regression analysis. |
